This paper presents studies in learning a form of organizational
knowledge called organizational roles in a multi-agent agent system.
It attempts to demonstrate the viability and utility of
self-organization in an agent-based system involving complex
interactions within the agent set. We present a multi-agent
parametric design system called L-TEAM where a set of heterogeneous
agents learn their organizational roles in negotiated search for
mutually acceptable designs. We tested the system on a steam condenser
design domain and empirically demonstrated its usefulness. L-TEAM
produced better results than its non-learning predecessor, TEAM, which
required elaborate knowledge engineering to hand-code organizational
roles for its agent set. In addition, we discuss experiments with
L-TEAM that highlight the importance of certain learning issues in
multi-agent systems.

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